A Sequence Transformation Model for Chinese Named Entity Recognition

Chinese Named Entity Recognition (NER), as one of basic natural language processing tasks, is still a tough problem due to Chinese polysemy and complexity. In recent years, most of previous works regard NER as a sequence tagging task, including statistical models and deep learning methods. In this paper, we innovatively consider NER as a sequence transformation task in which the unlabeled sequences (source texts) are converted to labeled sequences (NER labels). In order to model this sequence transformation task, we design a sequence-to-sequence neural network, which combines a Conditional Random Fields (CRF) layer to efficiently use sentence level tag information and the attention mechanism to capture the most important semantic information of the encoded sequence. In experiments, we evaluate different models both on a standard corpus consisting of news data and an unnormalized one consisting of short messages. Experimental results showed that our model outperforms the state-of-the-art methods on recognizing short interdependence entity.